{"id":58444,"date":"2023-07-17T08:00:51","date_gmt":"2023-07-17T15:00:51","guid":{"rendered":"https:\/\/phisonblog.com\/?p=58444"},"modified":"2025-07-22T09:27:05","modified_gmt":"2025-07-22T16:27:05","slug":"role-of-ssds-in-ai-and-machine-learning","status":"publish","type":"post","link":"https:\/\/phisonblog.com\/zh-tw\/role-of-ssds-in-ai-and-machine-learning\/","title":{"rendered":"SSD \u5728\u4eba\u5de5\u667a\u80fd\u548c\u6a5f\u5668\u5b78\u7fd2\u4e2d\u7684\u4f5c\u7528"},"content":{"rendered":"

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The terms artificial intelligence (AI)<\/a> and machine learning (ML) are being used more and more in the computing industry, but even experienced IT practitioners may not be fully aware of the computing and storage infrastructure required to support the two technologies. This article examines the issue and offers insights into the ways that solid state drives (SSDs) enable the best AI and ML outcomes.<\/p>\n

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Read: AI-Driven Data Engagement: What It Is and How It Benefits Businesses<\/div><\/div><\/div><\/div><\/div>\n

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What are AI and ML?<\/h3>\n

The first step in understanding the true natures of AI and ML is to grasp that they are not the same thing. AI is about creating software that can think like a human being. ML involves getting software to learn new concepts and then to continue getting better at mastering these concepts. They are distinct but related, overlapping technologies.<\/p>\n

AI and ML not new ideas, either. The computer visionary Alan Turing posited that machines could be made to think like people back in 1950. By 1959, AI pioneer Marvin Minsky was administering the MIT freshman calculus exam to a very early AI program. It passed. Movies have given us the murderously intelligent HAL 9000 in 2001: A Space Odyssey<\/em> and the equally lethal Skynet in The Terminator<\/em>. These examples are worth mentioning because fiction has informed the way we think about AI and ML, while also causing some confusion along the way.<\/p>\n

Fortunately, we have not yet reached the age of Skynet, but our world is full of impressive examples of AI and ML at work. Most of these are not big or flashy, but no less impactful on business and our daily lives. A Robotic Process Automation (RPA) \u201cbot,\u201d for example, can use AI to perform tasks like reading email messages and filling out forms. ML drives processes like facial recognition in law enforcement or cancer diagnosis in the medical field.<\/p>\n

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How do AI and ML work?<\/h3>\n

While there are many varieties of AI and ML programming, at their cores, both technologies are based on pattern recognition. In the RPA email reading example, the bot is trained to recognize phrases in an email message that describe what it\u2019s about. A message containing the words \u201cpayment\u201d or \u201coverdue\u201d is meant for the accounting department.<\/p>\n

The bot can also parse the email signature and use pattern recognition to determine if the message comes from a vendor (accounts payable) or a customer (accounts receivable). This type of capability is also useful in cybersecurity, wherein AI software can examine millions of data points coming from security logs and spot anomalous behavior that indicates an attack is underway.<\/p>\n

ML similarly utilizes pattern recognition to get better at understanding a given area of knowledge. ML systems can learn about data and continuously get \u201csmarter\u201d without having to follow programmed code or specific rules. For example, an ML algorithm can \u201clook at\u201d a million images of trees and plants. At some point, the algorithm will teach itself the difference between a tree and a plant. The essential difference between AI and ML, therefore, is that AI has been taught to spot patterns while ML is still learning and getting better at spotting patterns.<\/p>\n

All of this requires the handling of enormous amounts of data. To a certain extent, AI and ML are simply extensions of the big data paradigm. Big data and data analytics make it possible to interpret large, diverse data sets, discover visual trends and come up with new insights. AI and ML take the process a step further. They leverage existing big data analytics and data science processes, such as data mining, statistical analysis and predictive modeling to enable inferences, decision making and action steps based on big data.<\/p>\n

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Practically speaking, AI and ML comprises four separate processes, each of which involves data management:<\/p>\n